A cut-set is a collection of possible functional sub-component failures or degradations that could lead to a possible failure event or degradation of a higher-level component or function. Cut-set analysis can be applied to any system such as automobiles or airplanes to identify and rank system dependencies during the design phase. In particular the method is suited to analyze large, highly integrated systems, such as in automobiles, aircraft or networked systems such as data or command and control networks.
The process of cut-set generation is vital to determine reliability of critical components. However, the cut-set generation process can be time consuming when applied to large systems made of a great number of components.
Most of the existing solutions for system reliability assessment make use of Fault Tree Analysis (FTA), a method which is time consuming.
Other solutions use an algorithm for cut-set generation, wherein all traces leading to the condition in question are calculated for each sub-condition analyzed. This method is still time consuming for large systems.
When using model-based methods for reliability assessment as part of large, highly integrated system architecture development, such as an airplane, a large number of conditions need to be assessed. Moreover, each condition can have a large number of potential causes (e.g., cut-sets), containing single or typically multiple component reliability issues. Producing an exhaustive list of cut-sets for large systems can be time consuming but is important in order to harvest the benefits of model-based methods. The present disclosure improves the efficiency of model-based cut-set generation.